# Live Person Assignment

Our greedy switching considers a switch valid if and only if it is better than the current assignment solution, in terms of the problem’s global objective function.DGS is partially greedy as it ranks the potential switches in order of the greatest improvement a given switch would provide over the current assignment.

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DGS starts with a random initial solution and then the algorithm iteratively tries to find a better solution through searching within a well defined neighborhood.Authors: Amgad Naiem, Mohammed El-Beltagy INTRODUCTION The linear sum assignment problem (LSAP) is a classical combinatorial optimization problems that is found in many applications.The assignment problem deals with the question of how to assign n persons to n objects in the best possible way.The linear sum assignment problem can be found in many real world problems; from simple personnel to tasks assignment in a factory or an organization to more complex applications such as peer-to-peer (P2P) satellite refueling.It occurs in fields as varied as bioinformatics and computer vision.The assignment problem is specified by the cost matrix cij for minimization problems or the benefit matrix aij for maximization problems.Those matrices describe the cost or benefit of assigning object j to person i.approximate dual projective algorithm by Ramakrishnan et al.) and algorithms that use forest construction as dual forest algorithm by Achatz et al.The auction algorithm is considered one of the fastest algorithm that can find the optimal solution for the assignment problem.In the same section we will explore optimality aspects of our algorithm.We will present the modifications to the basic DGS for assignment problems with partial connectivity in section III and a parallelization approach of the DGS algorithm on GPUs is presented in section IV.

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